• Corpus ID: 54089884

Challenging Common Assumptions in the Unsupervised Learning of Disentangled Representations

@article{Locatello2019ChallengingCA,
  title={Challenging Common Assumptions in the Unsupervised Learning of Disentangled Representations},
  author={Francesco Locatello and Stefan Bauer and Mario Lucic and Sylvain Gelly and Bernhard Sch{\"o}lkopf and Olivier Bachem},
  journal={ArXiv},
  year={2019},
  volume={abs/1811.12359}
}
The key idea behind the unsupervised learning of disentangled representations is that real-world data is generated by a few explanatory factors of variation which can be recovered by unsupervised learning algorithms. [] Key Method We first theoretically show that the unsupervised learning of disentangled representations is fundamentally impossible without inductive biases on both the models and the data.
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735 Citations
Highly Influential Citations
131
Background Citations
496
Methods Citations
171
Results Citations
45

735 Citations

Citation Type

Citation Type

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References

SHOWING 1-10 OF 72 REFERENCES
Learning Deep Disentangled Embeddings with the F-Statistic Loss
TLDR
A new paradigm for discovering disentangled representations of class structure is proposed and a novel loss function based on the $F$ statistic is proposed, which describes the separation of two or more distributions.
Learning Disentangled Representations with Semi-Supervised Deep Generative Models
TLDR
This work proposes to learn disentangled representations that encode distinct aspects of the data into separate variables using model architectures that generalise from standard VAEs, employing a general graphical model structure in the encoder and decoder.
A Framework for the Quantitative Evaluation of Disentangled Representations
TLDR
A framework for the quantitative evaluation of disentangled representations when the ground-truth latent structure is available is proposed and three criteria are explicitly defined and quantified to elucidate the quality of learnt representations and thus compare models on an equal basis.
Multi-Level Variational Autoencoder: Learning Disentangled Representations from Grouped Observations
TLDR
The Multi-Level Variational Autoencoder (ML-VAE), a new deep probabilistic model for learning a disentangled representation of a set of grouped observations, separates the latent representation into semantically meaningful parts by working both at the group level and the observation level, while retaining efficient test-time inference.
Interventional Robustness of Deep Latent Variable Models
TLDR
The interventional robustness score is introduced, which provides a quantitative evaluation of robustness in learned representations with respect to interventions on generative factors and changing nuisance factors, and how this score can be estimated from labeled observational data, that may be confounded, and further provide an efficient algorithm that scales linearly in the dataset size.
beta-VAE: Learning Basic Visual Concepts with a Constrained Variational Framework
Learning an interpretable factorised representation of the independent data generative factors of the world without supervision is an important precursor for the development of artificial…
Variational Inference of Disentangled Latent Concepts from Unlabeled Observations
TLDR
This work considers the problem of unsupervised learning of disentangled representations from large pool of unlabeled observations, and proposes a variational inference based approach to inferdisentangled latent factors.
Competitive Training of Mixtures of Independent Deep Generative Models
TLDR
This work considers mixtures of implicit generative models that ``disentangle'' the independent generative mechanisms underlying the data and proposes a competitive training procedure in which the models only need to capture the portion of the data distribution from which they can produce realistic samples.
Recent Advances in Autoencoder-Based Representation Learning
TLDR
An in-depth review of recent advances in representation learning with a focus on autoencoder-based models and makes use of meta-priors believed useful for downstream tasks, such as disentanglement and hierarchical organization of features.
Robustly Disentangled Causal Mechanisms: Validating Deep Representations for Interventional Robustness
TLDR
A causal perspective on representation learning is provided which covers disentanglement and domain shift robustness as special cases and a new metric for the quantitative evaluation of deep latent variable models is introduced.
...
...